What are the Advantages and Limitations of Big Data Analytics?

In data analytics, companies examine and analyze the crucial information and draw a conclusion. The overall technique allows the companies to uncover the pattern and determine the insights. According to business protectives, data analytics is the best approach to determine the insights of finance, sales, operations, product development, and markets. Furthermore, it allows the business management team to interact with each other and improve their performance. Data analytics are the best way to improve business performance and optimize future goals. For instance, Edgar Radjabli adopted big data analytics after getting injured in a car accident. Data analytics helped him to focus on investment management. He used various algorithms and machine learning tools for trading strategies. Through the current example, the benefits and limitations of data analytics are listed below,

Better decisions: 

Almost all decisions are made based on gut feelings instead of data collections and facts in a business organization. Many businesses don’t have enough access to quality data resulting in poor decision-making. For this purpose, a company must transform the data into something meaningful for better business performance. Compared to good decisions, poor decisions hurt the business performance, impacting the growth and profitability of the business.

Increase efficiency of work:

When it comes to analytics, it helps recognize a large amount of data quickly and represent decently to accomplish goals. It allows the managers to share business insights with the employees and help them to perform better. The managers depict the gaps and improvement areas to the employees, increasing the productivity and efficiency of the workplace.

Lack of collaboration within a team: 

In the organization, the lack of collaboration among the team is the primary defect. Although data analytics helps the team and departments, it targets the selected team members to execute the tasks. In this way, the insights created by these teams are not of much value. The analytics team must focus on the correct decision by answering the right questions.

Little or zero patience and commitment: 

The implementation of analytics solutions may not be as complex as it seems. The real problems are that they are costly, and the return on investment is not immediate. If the existing data is not enough, it will require time to carry out the procedures and tasks to collect the data. Usually, the analytics models improve over time and demand a dedication to accomplishing the task. The results take time to appear, and businesses lose patience over time. As a result, it leads to model failure. When implementing data, the organization needs to understand the mechanism correctly. Furthermore, they must know about the correct actions necessary to fix the problems.

Less quality of data: 

The other limitation of big data analytics is the low access to quality data. Many businesses have accessible data, but there is always a question mark on the right and quality data. In this process, the best approach is to determine the correct data through proper means.

Like this article?

Share on facebook
Share on Facebook
Share on twitter
Share on Twitter
Share on linkedin
Share on Linkdin
Share on pinterest
Share on Pinterest